World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
17780
World Ranking
6658
National Ranking
2941

James Theiler publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where James Theiler sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 227 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

James Theiler D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where James Theiler sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

James Theiler is a researcher affiliated with Los Alamos National Laboratory in the United States. Their primary field of study is Medicine, with a particular focus on Infectious Diseases. Theiler's research spans several related subfields, including Molecular Biology, Animal Science and Zoology, Geophysics, and Media Technology.

Theiler's work predominantly explores topics related to SARS-CoV-2 and COVID-19, with significant contributions to understanding virus detection, clinical research, and epidemiological studies. They have also conducted research on animal virus infection studies and remote-sensing image classification, as well as seismic waves and analysis.

The frequent publication venues for Theiler's research include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Cell Host & Microbe
  • IEEE Signal Processing Magazine
  • Cell

Theiler's collaborations feature several coauthors working closely on related scientific topics. These frequent coauthors include:

  • Bette Korber
  • Will Fischer
  • Hyejin Yoon
  • Kshitij Wagh
  • Brian Foley

Their recent research papers demonstrate a focus on viral mutations, transmissibility, and neutralization:

  • Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus, 2020, Cell
  • Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2, 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral spike vaccines, 2021, Cell Host & Microbe
  • Multiple lineages of monkeypox virus detected in the United States, 2021-2022, 2022, Science
  • Substantial Neutralization Escape by SARS-CoV-2 Omicron Variants BQ.1.1 and XBB.1, 2023, New England Journal of Medicine

Best Publications

  • Testing for nonlinearity in time series: the method of surrogate data

    James Theiler;Stephen Eubank;André Longtin;Bryan Galdrikian

  • Estimating fractal dimension

    James Theiler

  • Spurious dimension from correlation algorithms applied to limited time-series data

    James Theiler

  • Generating Surrogate Data for Time Series with Several Simultaneously Measured Variables

    Dean Prichard;James Theiler

  • Accelerated search for materials with targeted properties by adaptive design.

    Dezhen Xue;Dezhen Xue;Prasanna V. Balachandran;John Hogden;James Theiler

  • Accurate on-line support vector regression

    Junshui Ma;James Theiler;Simon Perkins

  • Grafting: fast, incremental feature selection by gradient descent in function space

    Simon Perkins;Kevin Lacker;James Theiler

  • Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram.

    James Theiler;James Theiler;Paul E. Rapp

  • Efficient algorithm for estimating the correlation dimension from a set of discrete points

    James Theiler

  • Constrained-realization Monte-Carlo method for hypothesis testing

    James Theiler;James Theiler;Dean Prichard;Dean Prichard;Dean Prichard

  • Online feature selection using grafting

    Simon Perkins;James Theiler

  • Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery

    C.C. Funk;J. Theiler;D.A. Roberts;C.C. Borel

  • Adaptive Strategies for Materials Design using Uncertainties.

    Prasanna V. Balachandran;Dezhen Xue;Dezhen Xue;James Theiler;John Hogden

  • Using Surrogate Data to Detect Nonlinearity in Time Series

    J. Theiler;B. Galdrikian;A. Longtin;S. Eubank

  • Generalized redundancies for time series analysis

    Dean Prichard;Dean Prichard;James Theiler

  • Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware

    Mike Estlick;Miriam Leeser;James Theiler;John J. Szymanski

  • An Overview of Background Modeling for Detection of Targets and Anomalies in Hyperspectral Remotely Sensed Imagery

    Stefania Matteoli;Marco Diani;James Theiler

  • Don't Bleach Chaotic Data

    James Theiler;Stephen Eubank

  • Genetic algorithms and support vector machines for time series classification

    Damian R. Eads;Daniel Hill;Sean Davis;Simon J. Perkins

  • Statistical precision of dimension estimators.

    James Theiler;James Theiler

  • Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

    N.R. Harvey;J. Theiler;S.P. Brumby;S. Perkins

  • Nonlinear modeling of chaotic time series: Theory and applications

    M. Casdagli;S. Eubank;J.D. Farmer;J. Gibson

Frequent Co-Authors

Bette T. Korber
Bette T. Korber Los Alamos National Laboratory
Brendt Wohlberg
Brendt Wohlberg Los Alamos National Laboratory
Maya Gokhale
Maya Gokhale Lawrence Livermore National Laboratory
Anthony B. Davis
Anthony B. Davis California Institute of Technology
Beatrice H. Hahn
Beatrice H. Hahn University of Pennsylvania
Barton F. Haynes
Barton F. Haynes Duke University
Dan H. Barouch
Dan H. Barouch Harvard Medical School
Bing Chen
Bing Chen Harvard University
Norman L. Letvin
Norman L. Letvin Beth Israel Deaconess Medical Center
Roger Paredes
Roger Paredes University of Vic - Central University of Catalonia

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